Justificación de las plantas de transferencia

As a contribution, we propose to examine the differences and similarities in the coor- dination mechanisms that have just been outlined, in order to compare coordination in supply chain management with coordination in multi-agent systems. First, from the viewpoint of vocabulary, “cooperation” is used in these two fields, and the two words “collaboration” and “cooperation” refer to the same concept, but not in the same field. That is, companies in supply chains are said to collaborate, while agents cooperate.

Besides this little difference in vocabulary, we take the classes of multi-agent coor- dination techniques proposed byBoutilier [1996] as a basis of comparison of these two fields:

1. Communication-based coordination: Every coordination mechanism presented in Paragraph “Focus on Supply Chain Coordination through Stream Control” is a communication-based mechanism, because they use tokens, which are little pieces of information used to coordinate activities. We have presented in Subsection4.1.1 such token-based approaches, because they are decentralized like multi-agent sys- tems, which respects the autonomy of companies. Nevertheless, there also exist centralized approaches, which use communication too, but we do not insist on them, because centralization of decision requires companies to follow an external leader. In both cases of centralization and decentralization, we only note that coordination through stream control in supply chain always uses communication, like communication-based coordination in multi-agent systems.

2. convention-based coordination: This class of coordination mechanism corresponds to laws and contracts in supply chains:

• Laws: Laws imposed on a company by the society correspond exactly to social laws in multi-agent systems, because both are given from the exterior. Furthermore, in both cases, agents (respectively companies) may propose to the rest of the system (respectively society) to change these laws.

• Contracts: The little difference between convention-based coordination in multi-agent systems and contracts in supply chains, is that contracts have to be found by companies, while they are given a priori to agents. Therefore, contracts in a supply chain are mostly similar to learning-based coordination in multi-agent systems, because finding contracts can be seen as a learning process.

3. learning-based coordination: The link between learning-based coordination and contracts in the supply chain is stronger than the link outlined with convention-

based coordination: when agents learn to coordinate, their learning finds the best contracts ruling their interactions with other agents. In the same way, managers in supply chains “learn” which contracts are needed by their supply chains. When the environment changes, agents learn in order to find new contracts, which cor- responds to contract updating in supply chains.

As we can see, there are some similarities between the coordination mechanisms in multi-agent systems and two (among four) of the mechanisms in supply chains. The two other mechanisms (“discounts” and “joint optimization”) in supply chains also have correspondances in multi-agent systems:

• Discounts: Discounts are specific to systems managing money, and thus, are applied in some specific applications of multi-agent systems, e.g., e-commerce. More generally, market mechanisms are used to coordinate some multi-agent sys- tems, and in particular, mechanism design may propose discounts in order to make the overall system works in the best way [Eymann, 2001]. For example, some research studies the way to price some shared resources, such as a net- works [MacKie-Mason and Varian, 1995], in order to avoid congestions.

• Joint optimization: This approach proposes centralizing decision making, which is the contrary of the multi-agent philosophy, since this philosophy is to decentral- ize decision and control. At first glance, companies also prefer decentralization, because they prefer to keep their autonomy, but studying centralized decision making is interesting for at least two reasons:

– companies may accept centralized decision making, i.e., they may accept to loose some control of their activities, if it can be proven that decentralization is much worse for them.

– centralizing decision making allows using mathematical tools, such as Op- erations Research [Hillier and Lieberman, 1997], to find the best possible decision, which can be used as a reference to measure the efficiency of a decentralized approach to coordinate companies.

Similarly, in the field of multi-agent systems, Durfee[2001] proposed optimal- ity as one of the possible metrics of coordination. That is, an optimization is said to be optimal, if we can show that no other coordination leads to better results. Even though optimality is desirable, it is rarely feasible, because it requires a great deal of computation and many communications. Moreover, Durfee also proposed some other characteristics of coordination, that are also interesting for coordination in the field of supply chains. Here are some examples of such characteristics:

∗ scalability: how does the performance of the system change when agents are added?

∗ heterogeneity: how does the performance of the system change when the differences of agent types increase?

∗ robustness: how does the performance of the system evolve when there are changes in the world, while some assumptions about this world were stated to design the coordination mechanism?

∗ overheads: what are the computational and the communication over- heads of the coordination mechanism?

Although centralized decision making is not desirable neither in supply chains, nor in multi-agent systems, both fields also consider it. Indeed, we have seen that agents can make joint decisions in a similar way to joint optimization in supply chain management. Such a joint decision was referred as “coordination through joint intentions” in Subsection 4.1.2. We have noted that this coordination is in fact cooperation, because agents want to work together. In the context of supply chains, “joint optimization” also refers to cooperation, and more specifically to collaboration, since this word is preferred in supply chain management. As a consequence, the aims of joint intention and joint optimization are the same, i.e., to collaborate/cooperate, even if the means are different, i.e., centralized coordination based on mathematics vs. sharing of a certain mental state.

This section has introduced and compared coordination in two particular types of distributed system. We now present game theory, a formal tool to study coordination, and more generally, interaction between agents.